Unhealthy eating increases the risk of developing several kinds of cancer. This occurs directly through consumption of carcinogenic food, and indirectly through overweight and obesity. Because nearly 70% of American adults are overweight or obese, it is critical to develop effective interventions to alter eating behavior. One key factor that influences eating behavior and weight gain is cue-induced food craving. Craving stimulates appetitive motivation to eat, but can be regulated via cognitive strategies such as reappraisal, or the reconstrual of a stimulus to change its affective meaning. Reappraisal increases the salience of consumption- related costs and reduces food craving for unhealthy food. Craving reappraisal is therefore a promising target for interventions designed to reduce unhealthy eating and risk for diet-related cancers. However, individual differences in treatment efficacy remain a persistent problem with interventions. To understand why an intervention works for some individuals and not for others requires clearly defined neurobiological mechanisms of change, as well as sensitive and specific tools to evaluate individual differences in psychological targets. To fill this gap, the goal of this project is to leverage machine learning and multivariate neuroimaging methods to develop and validate a sensitive and specific neural signature of craving reappraisal that can be used as a neurobiological index of craving reappraisal ability. To achieve this goal, this project will pursue the following Aims: 1) develop and validate a neural signature of craving reappraisal in an independent sample of existing data, and 2) establish the predictive and incremental validity the neural signature in the context of an ongoing randomized control trial of cognitive reappraisal training to reduce unhealthy eating in overweight and obese adults. Specifically, after development, I will test the construct validity of the neural signature by assessing whether expression of the signature is greater while participants reappraise their desire for craved food than while they simply view these foods (Aim 1). I will also test the predictive and incremental validity of the neural signature by assessing the extent to which individual differences the neural signature change predict intervention outcomes, such as the value of unhealthy food and eating behavior, above and beyond standard methods (Aim 2). Upon completion of this project, I will have developed and validated a sensitive and specific neurobiological index of craving reappraisal ability that can be readily used by other researchers to evaluate intervention efficacy and individual differences in responsivity to treatment. I will also receive in-depth training in translational neuroscience interventions for cancer control, and multivariate neuroimaging and machine learning. This work will facilitate the refinement of reappraisal-based interventions to reduce unhealthy eating that will ultimately reduce the prevalence of overweight and obesity and risk for diet-related cancers. Further, by documenting my analysis process and sharing my analysis code, the results of this work can readily be adopted by others to study a variety of psychological processes relevant to eating behavior and cancer risk.